The Prevalence and Associated Predictors for Diabetes Mellitus in Adult Patients With Thyroid Nodules
Bibliographic record
Abstract
Background: Diabetes mellitus (DM) and thyroid nodules (TNs) with the risk of malignancy are increasing globally. Hence, we conducted this study to evaluate the prevalence and the associated predictors for DM among adult patients with TNs in Royal Commission Hospital, Kingdom of Saudi Arabia (KSA). Methods: A retrospective study was conducted between January 1, 2015 and December 31, 2021. Patients with documented TNs based on the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) were recruited. Then the prevalence and associated risk factors for DM were assessed. Result: Three hundred ninety-one patients who had TNs were recruited. The median (interquartile range (IQR)) age was 46.00 (20.0) years, and 332 (84.9%) of the patients were females. There was a high prevalence of DM (24.0%) among adult patients with TNs. In the univariate analysis, there were significant associations between diagnosed DM among adult patients with TNs and age, gender, 25-hydroxyvitamin D (25(OH)D) level, hypertension, bronchial asthma, free triiodothyronine (FT3), white blood cell count, low-density lipoprotein (LDL), high-density lipoprotein (HDL) and triglycerides. In the multivariate analysis, there were significant associations between diagnosed DM among adult patients with TNs and age (odds ratio (OR) 1.037 (95% confidence interval (CI) 1.012 - 1.062)), hypertension (OR 0.374 (95% CI 0.203 - 0.689)), FT3 level (OR 0.635 (95% CI 0.412 - 0.980)), LDL (OR 0.643 (95% CI 0.456 - 0.907)) and HDL (OR 0.654 (95% CI 0.465 - 0.919)). Conclusion: There was a high prevalence of DM among patients with TNs. Age, hypertension, FT3, LDL and HDL were significantly associated with DM and TNs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".